Yes, data augmentation can work for tabular data, though it may require different techniques compared to image or text data. In tabular datasets, each row typically represents an individual observation with various numerical or categorical features. Since traditional augmentation methods like flipping or cropping don’t apply, developers need to employ strategies that can generate new rows while preserving the underlying distribution of the data.
One common approach involves synthetic data generation techniques. For example, you can use the SMOTE (Synthetic Minority Over-sampling Technique) algorithm, which creates new instances of the minority class in a classification problem. It does this by interpolating between existing points in the minority class to generate new examples. This can help balance the dataset and improve model performance, particularly in cases of class imbalance. Similarly, Random Oversampling or Random Undersampling can also be used to artificially augment data by duplicating instances or removing excess instances from the majority class, respectively.
Another technique developers can explore is feature manipulation. This might include adding noise to numerical features, combining features, or even generating new categorical feature levels. For instance, if you have a feature representing an individual's age, you might add a small random value to create a slightly modified version of that entry. Care should be taken to ensure that the augmented data still fits within realistic bounds of the data's original context. Overall, while data augmentation is less straightforward for tabular data, with thoughtful methods tailored to the structure of the data, it can effectively enhance model training and performance.